PRODUCTION SCHEDULING SIMULATION FOR DECREASING COMPLETION TIME AND IDLE TIME IN PRODUCTION SITE X KIMIA FARMA PLANT JAKARTA
Eackground and purpose :Efficiency and effectivity is a business concern for all utical industry. One of the way to prove about efficiency and effectivity is scheduling of the production. Kimia Farma Plant Jakarta have several facility of the .zduction included production X Facility. Scheduling of t...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/79002 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Eackground and purpose :Efficiency and effectivity is a business concern for all utical industry. One of the way to prove about efficiency and effectivity is scheduling of the production. Kimia Farma Plant Jakarta have several facility of the .zduction included production X Facility. Scheduling of the production would be imply in on x Facility. The parameter that would be researched is Talk Jime and Finishing 'Time. The Lowest Talk Time and The Fastest Finishing Time would be preferable. Method ed Result : Production scheduling need lead time retrosbectives data in every each u•kstation of the production. thus lead time would be compiled and eliminate the out layer Ota. Motecarlo simulation would be run with using random cumulative percentage. Simulation would be implied in end to end production. starting in weighing until secondary pzckaging. Simulation would be running 50 times. That would be curtailment in this running sunulation such as: This simulation only running 5 product with different flow process in 1 run of simulation. every each of production process consist of major cleaning and minor g that have different lead time. cleaning major would be implied when changing product or running the same product after 10 times consecutively. every work station has a different setuptime. Cleaning and set up in granulation and drying would be merge in one kad time because the process in the same room and so do sieving and final mixing. The result is the optimal sequencing is Product B, Product A, Product C, Product D and Product E because it has the lowest talktime (idletime) and the fastest finishing time. This simulation run in 388 batch product. Conclusion: The result is the-optimal sequencing is Product B,
Product A. Product C. Product D. dan Product E because it has the lowest talktime (idletime)
I and the fastest completion time
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